Advanced search
Start date
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Adaptive algorithms applied to accelerometer biometrics in a data stream context

Full text
Pisani, Paulo Henrique ; Lorena, Ana Carolina ; de Carvalho, Andre C. P. L. F.
Total Authors: 3
Document type: Journal article
Source: Intelligent Data Analysis; v. 21, n. 2, p. 353-370, 2017.
Web of Science Citations: 3

The use of smartphone devices has increased over the last years, as illustrated by the growth in smartphone sales. These devices are currently used for several services, such as bank account access, social networks and storage of personal information. In view of this scenario, an important question arises: does authentication mechanisms already present in these devices provide enough security? Recently, a new authentication method, named accelerometer biometrics, has been proposed. This method allows the authentication of users using accelerometer data, which can be obtained from accelerometers usually present in modern smartphones. This is a clear advantage of this biometric modality, as there would be no additional cost with hardware. However, as a behavioral biometric technology, user models induced from accelerometer data may become outdated over time. This paper investigates the use of adaptation mechanisms to update user models in accelerometer biometrics in a data stream context. Practical issues regarding the usage of accelerometer data are also discussed. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 12/25032-0 - Biometrics in a data flow context with immune algorithms
Grantee:Paulo Henrique Pisani
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 12/22608-8 - Use of data complexity measures in the support of supervised machine learning
Grantee:Ana Carolina Lorena
Support type: Research Grants - Young Investigators Grants